{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d58bfcbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e9ff070",
   "metadata": {},
   "outputs": [
    {
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       "      <th>today_dead</th>\n",
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       "      <th>today_input</th>\n",
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       "      <th>0</th>\n",
       "      <td>9577772</td>\n",
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       "      <td>2021-05-13 08:31:01</td>\n",
       "      <td>中国</td>\n",
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       "      <td>日本本土</td>\n",
       "      <td>660300</td>\n",
       "      <td>0</td>\n",
       "      <td>568402</td>\n",
       "      <td>11148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-05-13 00:00:47</td>\n",
       "      <td>泰国</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1983.0</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "        id       lastUpdateTime  name  total_confirm  total_suspect  \\\n",
       "0  9577772  2021-05-13 08:30:51   突尼斯         324103              0   \n",
       "1  9507896  2021-05-13 08:30:52  塞尔维亚         703497              0   \n",
       "2        0  2021-05-13 08:31:01    中国         103902              0   \n",
       "3        1  2021-05-13 08:30:51  日本本土         660300              0   \n",
       "4        2  2021-05-13 00:00:47    泰国          88907              0   \n",
       "\n",
       "   total_heal  total_dead  total_severe  total_input  today_confirm  \\\n",
       "0      283270       11637             0          0.0         1105.0   \n",
       "1           0        6611             0          0.0         1046.0   \n",
       "2       98532        4858             0       5810.0           32.0   \n",
       "3      568402       11148             0          0.0            1.0   \n",
       "4       26873         486             0          0.0         1983.0   \n",
       "\n",
       "   today_suspect  today_heal  today_dead  today_severe  today_storeConfirm  \\\n",
       "0            NaN      1716.0        81.0           NaN                 NaN   \n",
       "1            NaN         0.0        17.0           NaN                 NaN   \n",
       "2            NaN        26.0         0.0           NaN                 6.0   \n",
       "3            NaN         0.0        11.0           NaN                 NaN   \n",
       "4            NaN         0.0         0.0           NaN                 NaN   \n",
       "\n",
       "   today_input  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          9.0  \n",
       "3          NaN  \n",
       "4          NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "today_world = pd.read_csv(\"./today_world_2021_05_13.csv\")\n",
    "# 查看世界各国实时数据 \n",
    "today_world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "31f5690e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>当日新增死亡</th>\n",
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       "      <th>2</th>\n",
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       "      <td>2021-05-13 08:31:01</td>\n",
       "      <td>中国</td>\n",
       "      <td>103902</td>\n",
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       "      <td>32.0</td>\n",
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       "      <td>26.0</td>\n",
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       "      <td>9.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2021-05-13 08:30:51</td>\n",
       "      <td>日本本土</td>\n",
       "      <td>660300</td>\n",
       "      <td>0</td>\n",
       "      <td>568402</td>\n",
       "      <td>11148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-05-13 00:00:47</td>\n",
       "      <td>泰国</td>\n",
       "      <td>88907</td>\n",
       "      <td>0</td>\n",
       "      <td>26873</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "        编号                 更新时间    名称    累计确诊  累计疑似    累计治愈   累计死亡  累计重症  \\\n",
       "0  9577772  2021-05-13 08:30:51   突尼斯  324103     0  283270  11637     0   \n",
       "1  9507896  2021-05-13 08:30:52  塞尔维亚  703497     0       0   6611     0   \n",
       "2        0  2021-05-13 08:31:01    中国  103902     0   98532   4858     0   \n",
       "3        1  2021-05-13 08:30:51  日本本土  660300     0  568402  11148     0   \n",
       "4        2  2021-05-13 00:00:47    泰国   88907     0   26873    486     0   \n",
       "\n",
       "   累计境外输入  当日新增确诊  当日新增疑似  当日新增治愈  当日新增死亡  当日新增重症  当日现存确诊  当日境外输入  \n",
       "0     0.0  1105.0     NaN  1716.0    81.0     NaN     NaN     NaN  \n",
       "1     0.0  1046.0     NaN     0.0    17.0     NaN     NaN     NaN  \n",
       "2  5810.0    32.0     NaN    26.0     0.0     NaN     6.0     9.0  \n",
       "3     0.0     1.0     NaN     0.0    11.0     NaN     NaN     NaN  \n",
       "4     0.0  1983.0     NaN     0.0     0.0     NaN     NaN     NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_dict = {'date':'日期','name':'名称','id':'编号','lastUpdateTime':'更新时间',\n",
    "             'today_confirm':'当日新增确诊','today_suspect':'当日新增疑似',\n",
    "             'today_heal':'当日新增治愈','today_dead':'当日新增死亡',\n",
    "             'today_severe':'当日新增重症','today_storeConfirm':'当日现存确诊',\n",
    "             'today_input':'当日境外输入',\n",
    "             'total_confirm':'累计确诊','total_suspect':'累计疑似',\n",
    "             'total_heal':'累计治愈','total_dead':'累计死亡','total_severe':'累计重症',\n",
    "             'total_input':'累计境外输入'}\n",
    "\n",
    "# 更改列名\n",
    "today_world.rename(columns=name_dict,inplace=True)    # inplace参数判断是否在原数据上进行修改\n",
    "\n",
    "# 查看数据\n",
    "today_world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "506838ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 207 entries, 0 to 206\n",
      "Data columns (total 16 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   编号      207 non-null    object \n",
      " 1   更新时间    207 non-null    object \n",
      " 2   名称      207 non-null    object \n",
      " 3   累计确诊    207 non-null    int64  \n",
      " 4   累计疑似    207 non-null    int64  \n",
      " 5   累计治愈    207 non-null    int64  \n",
      " 6   累计死亡    207 non-null    int64  \n",
      " 7   累计重症    207 non-null    int64  \n",
      " 8   累计境外输入  170 non-null    float64\n",
      " 9   当日新增确诊  160 non-null    float64\n",
      " 10  当日新增疑似  13 non-null     float64\n",
      " 11  当日新增治愈  160 non-null    float64\n",
      " 12  当日新增死亡  160 non-null    float64\n",
      " 13  当日新增重症  13 non-null     float64\n",
      " 14  当日现存确诊  1 non-null      float64\n",
      " 15  当日境外输入  6 non-null      float64\n",
      "dtypes: float64(8), int64(5), object(3)\n",
      "memory usage: 26.0+ KB\n"
     ]
    }
   ],
   "source": [
    "today_world.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c9de5e6c",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>累计确诊</th>\n",
       "      <th>累计疑似</th>\n",
       "      <th>累计治愈</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.070000e+02</td>\n",
       "      <td>207.0</td>\n",
       "      <td>2.070000e+02</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>207.0</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.744855e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.054405e+05</td>\n",
       "      <td>16092.144928</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.176471</td>\n",
       "      <td>4542.956250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1500.743750</td>\n",
       "      <td>39.812500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.500000</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.123792e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.541818e+06</td>\n",
       "      <td>58871.155087</td>\n",
       "      <td>0.0</td>\n",
       "      <td>445.606659</td>\n",
       "      <td>28346.692286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6439.542956</td>\n",
       "      <td>215.728288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.674235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.260500e+03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.645000e+02</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.141900e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.794900e+04</td>\n",
       "      <td>775.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>381.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.376265e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.538625e+05</td>\n",
       "      <td>6332.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1547.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>225.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.358614e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.662023e+07</td>\n",
       "      <td>597785.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5810.000000</td>\n",
       "      <td>348421.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62091.000000</td>\n",
       "      <td>2494.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               累计确诊   累计疑似          累计治愈           累计死亡   累计重症       累计境外输入  \\\n",
       "count  2.070000e+02  207.0  2.070000e+02     207.000000  207.0   170.000000   \n",
       "mean   7.744855e+05    0.0  6.054405e+05   16092.144928    0.0    34.176471   \n",
       "std    3.123792e+06    0.0  2.541818e+06   58871.155087    0.0   445.606659   \n",
       "min    1.000000e+00    0.0  0.000000e+00       0.000000    0.0     0.000000   \n",
       "25%    1.260500e+03    0.0  2.645000e+02      23.500000    0.0     0.000000   \n",
       "50%    6.141900e+04    0.0  2.794900e+04     775.000000    0.0     0.000000   \n",
       "75%    3.376265e+05    0.0  2.538625e+05    6332.000000    0.0     0.000000   \n",
       "max    3.358614e+07    0.0  2.662023e+07  597785.000000    0.0  5810.000000   \n",
       "\n",
       "              当日新增确诊  当日新增疑似        当日新增治愈       当日新增死亡  当日新增重症  当日现存确诊  \\\n",
       "count     160.000000    13.0    160.000000   160.000000    13.0     1.0   \n",
       "mean     4542.956250     0.0   1500.743750    39.812500     0.0     6.0   \n",
       "std     28346.692286     0.0   6439.542956   215.728288     0.0     NaN   \n",
       "min         0.000000     0.0      0.000000     0.000000     0.0     6.0   \n",
       "25%        22.250000     0.0      0.000000     0.000000     0.0     6.0   \n",
       "50%       381.500000     0.0      0.000000     0.000000     0.0     6.0   \n",
       "75%      1547.250000     0.0    225.500000     8.000000     0.0     6.0   \n",
       "max    348421.000000     0.0  62091.000000  2494.000000     0.0     6.0   \n",
       "\n",
       "         当日境外输入  \n",
       "count  6.000000  \n",
       "mean   1.500000  \n",
       "std    3.674235  \n",
       "min    0.000000  \n",
       "25%    0.000000  \n",
       "50%    0.000000  \n",
       "75%    0.000000  \n",
       "max    9.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_world.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5afc8bf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "编号        0.000000\n",
       "更新时间      0.000000\n",
       "名称        0.000000\n",
       "累计确诊      0.000000\n",
       "累计疑似      0.000000\n",
       "累计治愈      0.000000\n",
       "累计死亡      0.000000\n",
       "累计重症      0.000000\n",
       "累计境外输入    0.178744\n",
       "当日新增确诊    0.227053\n",
       "当日新增疑似    0.937198\n",
       "当日新增治愈    0.227053\n",
       "当日新增死亡    0.227053\n",
       "当日新增重症    0.937198\n",
       "当日现存确诊    0.995169\n",
       "当日境外输入    0.971014\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_world.isnull().sum()/len(today_world)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "53e56504",
   "metadata": {},
   "outputs": [],
   "source": [
    "today_world['当日现存确诊'] = today_world['累计确诊']-today_world['累计治愈']-today_world['累计死亡']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aff608ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "编号        0.000000\n",
       "更新时间      0.000000\n",
       "名称        0.000000\n",
       "累计确诊      0.000000\n",
       "累计疑似      0.000000\n",
       "累计治愈      0.000000\n",
       "累计死亡      0.000000\n",
       "累计重症      0.000000\n",
       "累计境外输入    0.178744\n",
       "当日新增确诊    0.227053\n",
       "当日新增疑似    0.937198\n",
       "当日新增治愈    0.227053\n",
       "当日新增死亡    0.227053\n",
       "当日新增重症    0.937198\n",
       "当日现存确诊    0.000000\n",
       "当日境外输入    0.971014\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_world.isnull().sum()/len(today_world)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9832d3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 207 entries, 0 to 206\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   编号      207 non-null    object \n",
      " 1   更新时间    207 non-null    object \n",
      " 2   名称      207 non-null    object \n",
      " 3   累计确诊    207 non-null    int64  \n",
      " 4   累计疑似    207 non-null    int64  \n",
      " 5   累计治愈    207 non-null    int64  \n",
      " 6   累计死亡    207 non-null    int64  \n",
      " 7   累计重症    207 non-null    int64  \n",
      " 8   累计境外输入  170 non-null    float64\n",
      " 9   当日新增确诊  160 non-null    float64\n",
      " 10  当日新增疑似  13 non-null     float64\n",
      " 11  当日新增治愈  160 non-null    float64\n",
      " 12  当日新增死亡  160 non-null    float64\n",
      " 13  当日新增重症  13 non-null     float64\n",
      " 14  当日现存确诊  207 non-null    int64  \n",
      " 15  当日境外输入  6 non-null      float64\n",
      " 16  病死率     207 non-null    float64\n",
      "dtypes: float64(8), int64(6), object(3)\n",
      "memory usage: 27.6+ KB\n"
     ]
    }
   ],
   "source": [
    "today_world['病死率'] = (today_world['累计死亡']/today_world['累计确诊']).apply(lambda x: format(x, '.2f'))\n",
    "today_world['病死率'] = today_world['病死率'].astype('float')\n",
    "today_world.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a8b35a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>编号</th>\n",
       "      <th>更新时间</th>\n",
       "      <th>名称</th>\n",
       "      <th>累计确诊</th>\n",
       "      <th>累计疑似</th>\n",
       "      <th>累计治愈</th>\n",
       "      <th>累计死亡</th>\n",
       "      <th>累计重症</th>\n",
       "      <th>累计境外输入</th>\n",
       "      <th>当日新增确诊</th>\n",
       "      <th>当日新增疑似</th>\n",
       "      <th>当日新增治愈</th>\n",
       "      <th>当日新增死亡</th>\n",
       "      <th>当日新增重症</th>\n",
       "      <th>当日现存确诊</th>\n",
       "      <th>当日境外输入</th>\n",
       "      <th>病死率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9577772</td>\n",
       "      <td>2021-05-13 08:30:51</td>\n",
       "      <td>突尼斯</td>\n",
       "      <td>324103</td>\n",
       "      <td>0</td>\n",
       "      <td>283270</td>\n",
       "      <td>11637</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1716.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29196</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9507896</td>\n",
       "      <td>2021-05-13 08:30:52</td>\n",
       "      <td>塞尔维亚</td>\n",
       "      <td>703497</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6611</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>696886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-05-13 08:31:01</td>\n",
       "      <td>中国</td>\n",
       "      <td>103902</td>\n",
       "      <td>0</td>\n",
       "      <td>98532</td>\n",
       "      <td>4858</td>\n",
       "      <td>0</td>\n",
       "      <td>5810.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-05-13 08:30:51</td>\n",
       "      <td>日本本土</td>\n",
       "      <td>660300</td>\n",
       "      <td>0</td>\n",
       "      <td>568402</td>\n",
       "      <td>11148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-05-13 00:00:47</td>\n",
       "      <td>泰国</td>\n",
       "      <td>88907</td>\n",
       "      <td>0</td>\n",
       "      <td>26873</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        编号                 更新时间    名称    累计确诊  累计疑似    累计治愈   累计死亡  累计重症  \\\n",
       "0  9577772  2021-05-13 08:30:51   突尼斯  324103     0  283270  11637     0   \n",
       "1  9507896  2021-05-13 08:30:52  塞尔维亚  703497     0       0   6611     0   \n",
       "2        0  2021-05-13 08:31:01    中国  103902     0   98532   4858     0   \n",
       "3        1  2021-05-13 08:30:51  日本本土  660300     0  568402  11148     0   \n",
       "4        2  2021-05-13 00:00:47    泰国   88907     0   26873    486     0   \n",
       "\n",
       "   累计境外输入  当日新增确诊  当日新增疑似  当日新增治愈  当日新增死亡  当日新增重症  当日现存确诊  当日境外输入   病死率  \n",
       "0     0.0  1105.0     NaN  1716.0    81.0     NaN   29196     NaN  0.04  \n",
       "1     0.0  1046.0     NaN     0.0    17.0     NaN  696886     NaN  0.01  \n",
       "2  5810.0    32.0     NaN    26.0     0.0     NaN     512     9.0  0.05  \n",
       "3     0.0     1.0     NaN     0.0    11.0     NaN   80750     NaN  0.02  \n",
       "4     0.0  1983.0     NaN     0.0     0.0     NaN   61548     NaN  0.01  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90e754be",
   "metadata": {},
   "outputs": [],
   "source": [
    "today_world.set_index(['名称'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "83b0c056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "编号                          0\n",
       "更新时间      2021-05-13 08:31:01\n",
       "累计确诊                   103902\n",
       "累计疑似                        0\n",
       "累计治愈                    98532\n",
       "累计死亡                     4858\n",
       "累计重症                        0\n",
       "累计境外输入                 5810.0\n",
       "当日新增确诊                   32.0\n",
       "当日新增疑似                    NaN\n",
       "当日新增治愈                   26.0\n",
       "当日新增死亡                    0.0\n",
       "当日新增重症                    NaN\n",
       "当日现存确诊                    512\n",
       "当日境外输入                    9.0\n",
       "病死率                      0.05\n",
       "Name: 中国, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_world.loc['中国']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "927903f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 10 entries, 美国 to 德国\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   累计确诊    10 non-null     int64  \n",
      " 1   累计死亡    10 non-null     int64  \n",
      " 2   病死率     10 non-null     float64\n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 320.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "world_top10 = today_world.sort_values(['累计确诊'],ascending=False)[0:10]\n",
    "world_top10 = world_top10[[\"累计确诊\",\"累计死亡\",\"病死率\"]]\n",
    "world_top10.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c01a2fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 840x480 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['SimHei']    #正常显示中文\n",
    "plt.rcParams['figure.dpi'] = 120      #设置所有图片的清晰度\n",
    "\n",
    "# 绘制条形图\n",
    "world_top10.sort_values('累计确诊').plot.barh(subplots=True,layout=(1,3),sharex=False,\n",
    "                                             figsize=(7,4),legend=False,sharey=True)\n",
    "\n",
    "plt.tight_layout()   #调整子图间距\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "02bdf393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>编号</th>\n",
       "      <th>名称</th>\n",
       "      <th>更新时间</th>\n",
       "      <th>累计确诊</th>\n",
       "      <th>累计疑似</th>\n",
       "      <th>累计治愈</th>\n",
       "      <th>累计死亡</th>\n",
       "      <th>累计重症</th>\n",
       "      <th>累计境外输入</th>\n",
       "      <th>累计境外输入确诊</th>\n",
       "      <th>累计境外输入死亡</th>\n",
       "      <th>累计境外输入治愈</th>\n",
       "      <th>当日新增确诊</th>\n",
       "      <th>当日新增疑似</th>\n",
       "      <th>当日新增治愈</th>\n",
       "      <th>当日新增死亡</th>\n",
       "      <th>当日新增重症</th>\n",
       "      <th>当日现存确诊</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>420000</td>\n",
       "      <td>湖北</td>\n",
       "      <td>2021-05-13 00:00:47</td>\n",
       "      <td>68158</td>\n",
       "      <td>0</td>\n",
       "      <td>63640</td>\n",
       "      <td>4512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>810000</td>\n",
       "      <td>香港</td>\n",
       "      <td>2021-05-13 09:00:58</td>\n",
       "      <td>11814</td>\n",
       "      <td>0</td>\n",
       "      <td>11505</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>440000</td>\n",
       "      <td>广东</td>\n",
       "      <td>2021-05-13 09:00:58</td>\n",
       "      <td>2383</td>\n",
       "      <td>0</td>\n",
       "      <td>2321</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>310000</td>\n",
       "      <td>上海</td>\n",
       "      <td>2021-05-13 08:31:01</td>\n",
       "      <td>2025</td>\n",
       "      <td>0</td>\n",
       "      <td>1960</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230000</td>\n",
       "      <td>黑龙江</td>\n",
       "      <td>2021-05-13 00:00:46</td>\n",
       "      <td>1610</td>\n",
       "      <td>0</td>\n",
       "      <td>1597</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       编号   名称                 更新时间   累计确诊  累计疑似   累计治愈  累计死亡  累计重症  累计境外输入  \\\n",
       "0  420000   湖北  2021-05-13 00:00:47  68158     0  63640  4512     0       0   \n",
       "1  810000   香港  2021-05-13 09:00:58  11814     0  11505   210     0       0   \n",
       "2  440000   广东  2021-05-13 09:00:58   2383     0   2321     8     0       0   \n",
       "3  310000   上海  2021-05-13 08:31:01   2025     0   1960     7     0       0   \n",
       "4  230000  黑龙江  2021-05-13 00:00:46   1610     0   1597    13     0       0   \n",
       "\n",
       "   累计境外输入确诊  累计境外输入死亡  累计境外输入治愈  当日新增确诊  当日新增疑似  当日新增治愈  当日新增死亡  当日新增重症  \\\n",
       "0       NaN       NaN       NaN       0     NaN       0       0     NaN   \n",
       "1       NaN       NaN       NaN       2     NaN       2       0     NaN   \n",
       "2       NaN       NaN       NaN       2     NaN       5       0     NaN   \n",
       "3       NaN       NaN       NaN       2     NaN       5       0     NaN   \n",
       "4       NaN       NaN       NaN       0     NaN       0       0     NaN   \n",
       "\n",
       "   当日现存确诊  \n",
       "0       0  \n",
       "1       0  \n",
       "2      -3  \n",
       "3      -3  \n",
       "4       0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "today_province = pd.read_csv(\"./today_province_2021_05_13.csv\")\n",
    "\n",
    "# 创建中文列名字典\n",
    "name_dict = {'date':'日期','name':'名称','id':'编号','lastUpdateTime':'更新时间',\n",
    "             'today_confirm':'当日新增确诊','today_suspect':'当日新增疑似',\n",
    "             'today_heal':'当日新增治愈','today_dead':'当日新增死亡',\n",
    "             'today_severe':'当日新增重症','today_storeConfirm':'当日现存确诊',\n",
    "             'total_confirm':'累计确诊','total_suspect':'累计疑似',\n",
    "             'total_heal':'累计治愈','total_dead':'累计死亡','total_severe':'累计重症',\n",
    "             'total_input':'累计境外输入','total_newConfirm':'累计境外输入确诊',\n",
    "             'total_newDead':'累计境外输入死亡','total_newHeal':'累计境外输入治愈'}\n",
    "\n",
    "# 更改列名\n",
    "today_province.rename(columns=name_dict,inplace=True)    # inplace参数是否在原对象基础上进行修改\n",
    "\n",
    "today_province.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "232348fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 34 entries, 湖北 to 西藏\n",
      "Data columns (total 17 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   编号        34 non-null     int64  \n",
      " 1   更新时间      34 non-null     object \n",
      " 2   累计确诊      34 non-null     int64  \n",
      " 3   累计疑似      34 non-null     int64  \n",
      " 4   累计治愈      34 non-null     int64  \n",
      " 5   累计死亡      34 non-null     int64  \n",
      " 6   累计重症      34 non-null     int64  \n",
      " 7   累计境外输入    34 non-null     int64  \n",
      " 8   累计境外输入确诊  1 non-null      float64\n",
      " 9   累计境外输入死亡  1 non-null      float64\n",
      " 10  累计境外输入治愈  1 non-null      float64\n",
      " 11  当日新增确诊    34 non-null     int64  \n",
      " 12  当日新增疑似    0 non-null      float64\n",
      " 13  当日新增治愈    34 non-null     int64  \n",
      " 14  当日新增死亡    34 non-null     int64  \n",
      " 15  当日新增重症    0 non-null      float64\n",
      " 16  当日现存确诊    34 non-null     int64  \n",
      "dtypes: float64(5), int64(11), object(1)\n",
      "memory usage: 4.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "名称\n",
       "台湾     21\n",
       "广东      2\n",
       "上海      2\n",
       "香港      2\n",
       "天津      1\n",
       "内蒙古     1\n",
       "山东      1\n",
       "四川      1\n",
       "湖北      0\n",
       "山西      0\n",
       "Name: 当日新增确诊, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将各省名称设置为索引\n",
    "today_province.set_index('名称',inplace=True)\n",
    "\n",
    "today_province.info()\n",
    "# 查看全国新增确诊top10的地区\n",
    "new_top10 = today_province['当日新增确诊'].sort_values(ascending=False)[:10]\n",
    "\n",
    "new_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6729686b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制条形图和饼图\n",
    "fig,ax = plt.subplots(1,2,figsize=(10,5))\n",
    "\n",
    "new_top10.sort_values(ascending=True).plot.barh(fontsize=10,ax=ax[0])\n",
    "new_top10.plot.pie(autopct='%.1f%%',fontsize=10,ax=ax[1])\n",
    "\n",
    "plt.ylabel('')\n",
    "plt.title('全国新增确诊top10地区',size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bfd1c922",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看全国现存确诊人数top10的省市\n",
    "store_top10 = today_province['当日现存确诊'].sort_values(ascending=False)[:10]\n",
    "\n",
    "# 绘制条形图\n",
    "\n",
    "store_top10.sort_values(ascending=True).plot.barh(fontsize=10)\n",
    "\n",
    "plt.title('全国现存确诊top10地区',size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "88a7c799",
   "metadata": {},
   "outputs": [
    {
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       "      <th>日期</th>\n",
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       "           日期  更新时间    累计确诊  累计疑似   累计治愈  累计死亡  累计重症  累计境外输入  累计境外输入确诊  \\\n",
       "0  2021-03-14   NaN  102363     0  96990  4849   NaN    5146       524   \n",
       "1  2021-03-15   NaN  102411     0  97011  4849   NaN    5159       551   \n",
       "2  2021-03-16   NaN  102433     0  97038  4849   NaN    5163       546   \n",
       "3  2021-03-17   NaN  102450     0  97079  4849   NaN    5169       522   \n",
       "4  2021-03-18   NaN  102479     0  97112  4849   NaN    5179       518   \n",
       "\n",
       "   当日新增确诊  当日新增疑似  当日新增治愈  当日新增死亡  当日新增重症  当日现存确诊  当日现存境外输入  \n",
       "0      30       0       0       0     NaN       0         5  \n",
       "1      48       0       0       0     NaN       0        13  \n",
       "2      22       4       0       0     NaN       0         4  \n",
       "3      17       2       0       0     NaN       0         6  \n",
       "4      29       1       0       0     NaN       0        10  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
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    }
   ],
   "source": [
    "# 读取数据\n",
    "alltime_china = pd.read_csv(\"./alltime_China_2021_05_13.csv\")\n",
    "\n",
    "# 创建中文列名字典\n",
    "name_dict = {'date':'日期','name':'名称','id':'编号','lastUpdateTime':'更新时间',\n",
    "             'today_confirm':'当日新增确诊','today_suspect':'当日新增疑似',\n",
    "             'today_heal':'当日新增治愈','today_dead':'当日新增死亡',\n",
    "             'today_severe':'当日新增重症','today_storeConfirm':'当日现存确诊',\n",
    "             'today_input':'当日现存境外输入',\n",
    "             'total_input':'累计境外输入','total_storeConfirm':'累计境外输入确诊',\n",
    "             'total_confirm':'累计确诊','total_suspect':'累计疑似',\n",
    "             'total_heal':'累计治愈','total_dead':'累计死亡','total_severe':'累计重症'}\n",
    "\n",
    "# 更改列名\n",
    "alltime_china.rename(columns=name_dict,inplace=True)\n",
    "\n",
    "alltime_china.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "257f3fd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60 entries, 0 to 59\n",
      "Data columns (total 16 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   日期        60 non-null     object \n",
      " 1   更新时间      0 non-null      float64\n",
      " 2   累计确诊      60 non-null     int64  \n",
      " 3   累计疑似      60 non-null     int64  \n",
      " 4   累计治愈      60 non-null     int64  \n",
      " 5   累计死亡      60 non-null     int64  \n",
      " 6   累计重症      0 non-null      float64\n",
      " 7   累计境外输入    60 non-null     int64  \n",
      " 8   累计境外输入确诊  60 non-null     int64  \n",
      " 9   当日新增确诊    60 non-null     int64  \n",
      " 10  当日新增疑似    60 non-null     int64  \n",
      " 11  当日新增治愈    60 non-null     int64  \n",
      " 12  当日新增死亡    60 non-null     int64  \n",
      " 13  当日新增重症    0 non-null      float64\n",
      " 14  当日现存确诊    60 non-null     int64  \n",
      " 15  当日现存境外输入  60 non-null     int64  \n",
      "dtypes: float64(3), int64(12), object(1)\n",
      "memory usage: 7.6+ KB\n"
     ]
    }
   ],
   "source": [
    "alltime_china.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e98253d1",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>25%</th>\n",
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       "      <td>4851.000000</td>\n",
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       "      <td>4854.500000</td>\n",
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       "      <td>4857.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "       更新时间           累计确诊  累计疑似          累计治愈         累计死亡  累计重症  \\\n",
       "count   0.0      60.000000  60.0     60.000000    60.000000   0.0   \n",
       "mean    NaN  103140.050000   0.0  97795.500000  4853.800000   NaN   \n",
       "std     NaN     464.624595   0.0    441.400257     3.418792   NaN   \n",
       "min     NaN  102363.000000   0.0  96990.000000  4849.000000   NaN   \n",
       "25%     NaN  102710.750000   0.0  97476.500000  4851.000000   NaN   \n",
       "50%     NaN  103150.500000   0.0  97804.500000  4854.500000   NaN   \n",
       "75%     NaN  103537.250000   0.0  98153.500000  4857.000000   NaN   \n",
       "max     NaN  103902.000000   0.0  98532.000000  4858.000000   NaN   \n",
       "\n",
       "            累计境外输入    累计境外输入确诊     当日新增确诊     当日新增疑似  当日新增治愈  当日新增死亡  当日新增重症  \\\n",
       "count    60.000000   60.000000  60.000000  60.000000    60.0    60.0     0.0   \n",
       "mean   5459.016667  490.750000  26.150000   0.916667     0.0     0.0     NaN   \n",
       "std     203.311691   48.547941   8.098598   1.318598     0.0     0.0     NaN   \n",
       "min    5146.000000  381.000000  11.000000   0.000000     0.0     0.0     NaN   \n",
       "25%    5283.000000  467.250000  20.000000   0.000000     0.0     0.0     NaN   \n",
       "50%    5448.500000  505.500000  25.500000   0.000000     0.0     0.0     NaN   \n",
       "75%    5629.000000  529.000000  31.000000   1.000000     0.0     0.0     NaN   \n",
       "max    5810.000000  555.000000  48.000000   6.000000     0.0     0.0     NaN   \n",
       "\n",
       "       当日现存确诊   当日现存境外输入  \n",
       "count    60.0  60.000000  \n",
       "mean      0.0  11.150000  \n",
       "std       0.0   3.922231  \n",
       "min       0.0   4.000000  \n",
       "25%       0.0   9.000000  \n",
       "50%       0.0  11.000000  \n",
       "75%       0.0  13.250000  \n",
       "max       0.0  20.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据的统计信息\n",
    "alltime_china.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3f15e5c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60 entries, 0 to 59\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   日期        60 non-null     object \n",
      " 1   累计确诊      60 non-null     int64  \n",
      " 2   累计疑似      60 non-null     int64  \n",
      " 3   累计治愈      60 non-null     int64  \n",
      " 4   累计死亡      60 non-null     int64  \n",
      " 5   累计重症      0 non-null      float64\n",
      " 6   累计境外输入    60 non-null     int64  \n",
      " 7   累计境外输入确诊  60 non-null     int64  \n",
      " 8   当日新增确诊    60 non-null     int64  \n",
      " 9   当日新增疑似    60 non-null     int64  \n",
      " 10  当日新增治愈    60 non-null     int64  \n",
      " 11  当日新增死亡    60 non-null     int64  \n",
      " 12  当日现存确诊    60 non-null     int64  \n",
      " 13  当日现存境外输入  60 non-null     int64  \n",
      "dtypes: float64(1), int64(12), object(1)\n",
      "memory usage: 6.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# 缺失值处理\n",
    "\n",
    "# 计算当日现存确诊人数\n",
    "alltime_china['当日现存确诊'] = alltime_china['累计确诊']-alltime_china['累计治愈']-alltime_china['累计死亡']\n",
    "\n",
    "# 删除更新时间一列\n",
    "alltime_china.drop(['更新时间','当日新增重症'],axis=1,inplace=True)\n",
    "\n",
    "alltime_china.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6f8c6477",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将日期改成datetime格式\n",
    "alltime_china['日期'] = pd.to_datetime(alltime_china['日期'])\n",
    "\n",
    "# 设置日期为索引\n",
    "alltime_china.set_index('日期',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eb71538e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "累计确诊        102908.0\n",
       "累计疑似             0.0\n",
       "累计治愈         97615.0\n",
       "累计死亡          4851.0\n",
       "累计重症             NaN\n",
       "累计境外输入        5352.0\n",
       "累计境外输入确诊       442.0\n",
       "当日新增确诊          41.0\n",
       "当日新增疑似           0.0\n",
       "当日新增治愈           0.0\n",
       "当日新增死亡           0.0\n",
       "当日现存确诊         442.0\n",
       "当日现存境外输入        17.0\n",
       "Name: 2021-04-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alltime_china.loc['2021-04-04']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ad50bbd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import dates\n",
    "# 时间序列数据绘制折线图\n",
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "alltime_china['当日新增确诊'].plot(ax=ax, style='-',lw=1,color='c',marker='o',ms=3)\n",
    "\n",
    "ax.xaxis.set_major_locator(dates.MonthLocator())    #设置间距\n",
    "ax.xaxis.set_major_formatter(dates.DateFormatter('%b'))    #设置日期格式\n",
    "\n",
    "fig.autofmt_xdate()    #自动调整日期倾斜\n",
    "\n",
    "plt.title('全国新冠肺炎新增确诊病例折线图',size=15)\n",
    "plt.ylabel('人数')\n",
    "plt.grid(axis='y')\n",
    "plt.box(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2a80bae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>累计确诊</th>\n",
       "      <th>累计疑似</th>\n",
       "      <th>累计治愈</th>\n",
       "      <th>累计死亡</th>\n",
       "      <th>累计重症</th>\n",
       "      <th>累计境外输入</th>\n",
       "      <th>当日新增确诊</th>\n",
       "      <th>当日新增疑似</th>\n",
       "      <th>当日新增治愈</th>\n",
       "      <th>当日新增死亡</th>\n",
       "      <th>当日新增重症</th>\n",
       "      <th>当日现存确诊</th>\n",
       "      <th>当日新增境外输入</th>\n",
       "      <th>名称</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>突尼斯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>突尼斯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>突尼斯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>突尼斯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>突尼斯</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           日期  累计确诊  累计疑似  累计治愈  累计死亡  累计重症  累计境外输入  当日新增确诊  当日新增疑似  当日新增治愈  \\\n",
       "0  2020-03-03     1     0     0     0     0       0       1     0.0       0   \n",
       "1  2020-03-08     2     0     0     0     0       0       1     0.0       0   \n",
       "2  2020-03-09     5     0     0     0     0       0       3     0.0       0   \n",
       "3  2020-03-11     7     0     0     0     0       0       2     0.0       0   \n",
       "4  2020-03-12    13     0     0     0     0       0       6     0.0       0   \n",
       "\n",
       "   当日新增死亡  当日新增重症  当日现存确诊  当日新增境外输入   名称  \n",
       "0       0     NaN     NaN       NaN  突尼斯  \n",
       "1       0     0.0     NaN       0.0  突尼斯  \n",
       "2       0     0.0     NaN       0.0  突尼斯  \n",
       "3       0     0.0     NaN       0.0  突尼斯  \n",
       "4       0     0.0     NaN       0.0  突尼斯  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "alltime_world = pd.read_csv(\"./alltime_world_2021_05_13.csv\")\n",
    "\n",
    "# 创建中文列名字典\n",
    "name_dict = {'date':'日期','name':'名称','id':'编号','lastUpdateTime':'更新时间',\n",
    "             'today_confirm':'当日新增确诊','today_suspect':'当日新增疑似',\n",
    "             'today_heal':'当日新增治愈','today_dead':'当日新增死亡',\n",
    "             'today_severe':'当日新增重症','today_storeConfirm':'当日现存确诊',\n",
    "             'today_input':'当日新增境外输入',\n",
    "             'total_confirm':'累计确诊','total_suspect':'累计疑似',\n",
    "             'total_heal':'累计治愈','total_dead':'累计死亡','total_severe':'累计重症',\n",
    "             'total_input':'累计境外输入'}\n",
    "\n",
    "# 更改列名\n",
    "alltime_world.rename(columns=name_dict,inplace=True)\n",
    "\n",
    "alltime_world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2577a537",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 66002 entries, 0 to 66001\n",
      "Data columns (total 15 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   日期        66002 non-null  object \n",
      " 1   累计确诊      66002 non-null  int64  \n",
      " 2   累计疑似      66002 non-null  int64  \n",
      " 3   累计治愈      66002 non-null  int64  \n",
      " 4   累计死亡      66002 non-null  int64  \n",
      " 5   累计重症      66002 non-null  int64  \n",
      " 6   累计境外输入    66002 non-null  int64  \n",
      " 7   当日新增确诊    66002 non-null  int64  \n",
      " 8   当日新增疑似    12909 non-null  float64\n",
      " 9   当日新增治愈    66002 non-null  int64  \n",
      " 10  当日新增死亡    66002 non-null  int64  \n",
      " 11  当日新增重症    12509 non-null  float64\n",
      " 12  当日现存确诊    0 non-null      float64\n",
      " 13  当日新增境外输入  65806 non-null  float64\n",
      " 14  名称        66002 non-null  object \n",
      "dtypes: float64(4), int64(9), object(2)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# 查看数据基本信息\n",
    "alltime_world.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "38b57eab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>累计确诊</th>\n",
       "      <th>累计疑似</th>\n",
       "      <th>累计治愈</th>\n",
       "      <th>累计死亡</th>\n",
       "      <th>累计重症</th>\n",
       "      <th>累计境外输入</th>\n",
       "      <th>当日新增确诊</th>\n",
       "      <th>当日新增疑似</th>\n",
       "      <th>当日新增治愈</th>\n",
       "      <th>当日新增死亡</th>\n",
       "      <th>当日新增重症</th>\n",
       "      <th>当日现存确诊</th>\n",
       "      <th>当日新增境外输入</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.600200e+04</td>\n",
       "      <td>66002.000000</td>\n",
       "      <td>6.600200e+04</td>\n",
       "      <td>66002.000000</td>\n",
       "      <td>66002.000000</td>\n",
       "      <td>66002.000000</td>\n",
       "      <td>66002.000000</td>\n",
       "      <td>12909.000000</td>\n",
       "      <td>6.600200e+04</td>\n",
       "      <td>66002.000000</td>\n",
       "      <td>12509.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65806.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.536492e+05</td>\n",
       "      <td>4.149632</td>\n",
       "      <td>2.575593e+05</td>\n",
       "      <td>8519.216524</td>\n",
       "      <td>1.676328</td>\n",
       "      <td>20.585361</td>\n",
       "      <td>2428.994561</td>\n",
       "      <td>3.528314</td>\n",
       "      <td>1.898824e+03</td>\n",
       "      <td>50.469289</td>\n",
       "      <td>-0.581341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.722947e+06</td>\n",
       "      <td>270.024248</td>\n",
       "      <td>1.305549e+06</td>\n",
       "      <td>34768.344144</td>\n",
       "      <td>87.009978</td>\n",
       "      <td>284.054118</td>\n",
       "      <td>13009.937003</td>\n",
       "      <td>95.676476</td>\n",
       "      <td>2.758526e+04</td>\n",
       "      <td>221.226924</td>\n",
       "      <td>12.372097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.107425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-68210.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.227181e+06</td>\n",
       "      <td>-6464.000000</td>\n",
       "      <td>-464.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.403000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.229000e+03</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.994650e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.166450e+04</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.431532e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.412100e+04</td>\n",
       "      <td>2987.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>905.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.540000e+02</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.358614e+07</td>\n",
       "      <td>28942.000000</td>\n",
       "      <td>2.662023e+07</td>\n",
       "      <td>597785.000000</td>\n",
       "      <td>7365.000000</td>\n",
       "      <td>5810.000000</td>\n",
       "      <td>880962.000000</td>\n",
       "      <td>4008.000000</td>\n",
       "      <td>4.227231e+06</td>\n",
       "      <td>7057.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>723.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               累计确诊          累计疑似          累计治愈           累计死亡          累计重症  \\\n",
       "count  6.600200e+04  66002.000000  6.600200e+04   66002.000000  66002.000000   \n",
       "mean   3.536492e+05      4.149632  2.575593e+05    8519.216524      1.676328   \n",
       "std    1.722947e+06    270.024248  1.305549e+06   34768.344144     87.009978   \n",
       "min    0.000000e+00      0.000000  0.000000e+00       0.000000      0.000000   \n",
       "25%    2.403000e+03      0.000000  1.229000e+03      48.000000      0.000000   \n",
       "50%    1.994650e+04      0.000000  1.166450e+04     335.000000      0.000000   \n",
       "75%    1.431532e+05      0.000000  9.412100e+04    2987.750000      0.000000   \n",
       "max    3.358614e+07  28942.000000  2.662023e+07  597785.000000   7365.000000   \n",
       "\n",
       "             累计境外输入         当日新增确诊        当日新增疑似        当日新增治愈        当日新增死亡  \\\n",
       "count  66002.000000   66002.000000  12909.000000  6.600200e+04  66002.000000   \n",
       "mean      20.585361    2428.994561      3.528314  1.898824e+03     50.469289   \n",
       "std      284.054118   13009.937003     95.676476  2.758526e+04    221.226924   \n",
       "min        0.000000  -68210.000000      0.000000 -4.227181e+06  -6464.000000   \n",
       "25%        0.000000       7.000000      0.000000  0.000000e+00      0.000000   \n",
       "50%        0.000000     119.000000      0.000000  4.000000e+01      2.000000   \n",
       "75%        0.000000     905.000000      0.000000  5.540000e+02     16.000000   \n",
       "max     5810.000000  880962.000000   4008.000000  4.227231e+06   7057.000000   \n",
       "\n",
       "             当日新增重症  当日现存确诊      当日新增境外输入  \n",
       "count  12509.000000     0.0  65806.000000  \n",
       "mean      -0.581341     NaN      0.088290  \n",
       "std       12.372097     NaN      3.107425  \n",
       "min     -464.000000     NaN    -19.000000  \n",
       "25%        0.000000     NaN      0.000000  \n",
       "50%        0.000000     NaN      0.000000  \n",
       "75%        0.000000     NaN      0.000000  \n",
       "max        0.000000     NaN    723.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alltime_world.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "877dc66d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 66002 entries, 0 to 66001\n",
      "Data columns (total 15 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   日期        66002 non-null  datetime64[ns]\n",
      " 1   累计确诊      66002 non-null  int64         \n",
      " 2   累计疑似      66002 non-null  int64         \n",
      " 3   累计治愈      66002 non-null  int64         \n",
      " 4   累计死亡      66002 non-null  int64         \n",
      " 5   累计重症      66002 non-null  int64         \n",
      " 6   累计境外输入    66002 non-null  int64         \n",
      " 7   当日新增确诊    66002 non-null  int64         \n",
      " 8   当日新增疑似    12909 non-null  float64       \n",
      " 9   当日新增治愈    66002 non-null  int64         \n",
      " 10  当日新增死亡    66002 non-null  int64         \n",
      " 11  当日新增重症    12509 non-null  float64       \n",
      " 12  当日现存确诊    66002 non-null  int64         \n",
      " 13  当日新增境外输入  65806 non-null  float64       \n",
      " 14  名称        66002 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(3), int64(10), object(1)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# 将日期一列数据类型变为datetime\n",
    "alltime_world['日期'] = pd.to_datetime(alltime_world['日期'])\n",
    "\n",
    "# 计算当日现存确诊\n",
    "alltime_world['当日现存确诊'] = alltime_world['累计确诊']-alltime_world['累计治愈']-alltime_world['累计死亡']\n",
    "\n",
    "alltime_world.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7c6f465f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['突尼斯', '塞尔维亚', '中国', '日本本土', '泰国', '新加坡', '韩国', '澳大利亚', '德国', '美国',\n",
       "       '马来西亚', '越南', '圣巴泰勒米', '肯尼亚', '伊朗', '以色列', '毛利亚尼亚', '黎巴嫩', '克罗地亚',\n",
       "       '奥地利', '瑞士', '希腊', '毛里求斯', '爱沙尼亚', '北马其顿', '白俄罗斯', '立陶宛', '阿塞拜疆',\n",
       "       '美属维尔京群岛', '蒙古', '乌克兰', '波兰', '波黑', '蒙特塞拉特', '南非', '布隆迪', '南苏丹',\n",
       "       '马耳他', '摩尔多瓦', '保加利亚', '孟加拉', '阿尔巴尼亚', '巴勒斯坦', '科摩罗', '阿富汗',\n",
       "       '沙特阿拉伯', '新西兰', '塔吉克斯坦', '泽西岛', '叙利亚', '巴拿马', '古巴', '尼日利亚', '摩洛哥',\n",
       "       '塞内加尔', '老挝', '巴哈马', '马约特岛', '斯洛文尼亚', '卢森堡', '爱尔兰', '厄瓜多尔', '捷克',\n",
       "       '匈牙利', '法属圭亚那', '多哥共和国', '哥斯达黎加', '文莱', '法罗群岛', '马提尼克岛', '荷兰',\n",
       "       '巴西', '洪都拉斯', '乌拉圭', '秘鲁', '智利', '格陵兰', '圣巴托洛谬岛', '马尔代夫', '委内瑞拉',\n",
       "       '毛里塔尼亚', '纳米比亚', '法属留尼汪岛', '波多黎各', '加纳', '赤道几内亚', '几内亚', '卢旺达',\n",
       "       '格林纳达', '斯威士兰', '坦桑尼亚', '贝宁', '刚果（金）', '中非共和国', '利比里亚', '索马里',\n",
       "       '塞拉利昂', '乍得', '赞比亚', '巴巴多斯', '马里', '阿根廷', '法属波利尼西亚', '巴林', '莫桑比克',\n",
       "       '喀麦隆', '乌干达', '厄立特里亚', '刚果（布）', '津巴布韦', '丹麦', '阿鲁巴', '斐济', '伯利兹',\n",
       "       '缅甸', '塞浦路斯', '关岛', '科索沃', '圣皮埃尔岛和密克隆岛', '吉尔吉斯斯坦', '博茨瓦纳', '尼日尔',\n",
       "       '苏里南', '佛得角', '萨尔瓦多', '圭亚那', '尼加拉瓜', '冈比亚', '东帝汶', '巴基斯坦', '埃及',\n",
       "       '科威特', '斯洛伐克', '直布罗陀', '摩纳哥', '巴拉圭', '荷属安的列斯', '多米尼克', '乌兹别克斯坦',\n",
       "       '黑山', '危地马拉', '加蓬', '苏丹', '利比亚', '圣马丁岛', '土耳其', '巴布亚新几内亚', '多米尼加',\n",
       "       '约旦', '亚美尼亚', '圣基茨和尼维斯', '瓜德罗普', '安提瓜和巴布达', '玻利维亚', '哥伦比亚',\n",
       "       '圣文森特和格林纳丁斯', '圣卢西亚', '法国', '阿联酋', '加拿大', '印度', '英国', '意大利', '俄罗斯',\n",
       "       '菲律宾', '芬兰', '尼泊尔', '葡萄牙', '也门', '塞舌尔', '西班牙', '斯里兰卡', '阿尔及利亚',\n",
       "       '柬埔寨', '海地', '瑞典', '特里尼达和多巴哥', '吉布提', '圣多美与普林西比', '布基纳法索', '比利时',\n",
       "       '伊拉克', '马拉维', '冰岛', '几内亚比绍', '拉脱维亚', '不丹', '挪威', '印度尼西亚', '安哥拉',\n",
       "       '开曼群岛', '埃塞俄比亚', '梵蒂冈', '科特迪瓦', '卡塔尔', '莱索托', '格鲁吉亚', '墨西哥',\n",
       "       '圣马力诺', '哈萨克斯坦', '安道尔', '牙买加', '格恩西岛', '罗马尼亚', '阿曼', '列支敦士登',\n",
       "       '马达加斯加'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看唯一值,可使用len()查看个数\n",
    "alltime_world['名称'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fa6f1cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-06-08    207\n",
       "2020-05-24    207\n",
       "2020-05-27    207\n",
       "2020-05-30    206\n",
       "2020-06-05    206\n",
       "2020-05-31    206\n",
       "2020-06-10    206\n",
       "2020-06-11    206\n",
       "2020-06-01    206\n",
       "2020-06-03    205\n",
       "2020-05-19    205\n",
       "2020-05-12    205\n",
       "2020-05-14    205\n",
       "2020-05-26    205\n",
       "2020-06-06    204\n",
       "2020-05-25    204\n",
       "2020-05-20    204\n",
       "2020-06-04    204\n",
       "2020-05-18    203\n",
       "2020-04-21    202\n",
       "Name: 日期, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计每天有多少国家出现疫情\n",
    "alltime_world['日期'].value_counts().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ca5f1dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置日期索引\n",
    "alltime_world.set_index('日期',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "be0d7bd8",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>累计确诊</th>\n",
       "      <th>累计疑似</th>\n",
       "      <th>累计治愈</th>\n",
       "      <th>累计死亡</th>\n",
       "      <th>累计重症</th>\n",
       "      <th>累计境外输入</th>\n",
       "      <th>当日新增确诊</th>\n",
       "      <th>当日新增疑似</th>\n",
       "      <th>当日新增治愈</th>\n",
       "      <th>当日新增死亡</th>\n",
       "      <th>当日新增重症</th>\n",
       "      <th>当日现存确诊</th>\n",
       "      <th>当日新增境外输入</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th>名称</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-20</th>\n",
       "      <th>韩国</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-21</th>\n",
       "      <th>韩国</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <th>韩国</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <th>韩国</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <th>韩国</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               累计确诊  累计疑似  累计治愈  累计死亡  累计重症  累计境外输入  当日新增确诊  当日新增疑似  当日新增治愈  \\\n",
       "日期         名称                                                                 \n",
       "2020-01-20 韩国     1     0     0     0     0       0       1     0.0       0   \n",
       "2020-01-21 韩国     1     0     0     0     0       0       0     0.0       0   \n",
       "2020-01-22 韩国     1     0     0     0     0       0       0     0.0       0   \n",
       "2020-01-23 韩国     1     0     0     0     0       0       0     0.0       0   \n",
       "2020-01-24 韩国     2     0     0     0     0       0       1     0.0       0   \n",
       "\n",
       "               当日新增死亡  当日新增重症  当日现存确诊  当日新增境外输入  \n",
       "日期         名称                                    \n",
       "2020-01-20 韩国       0     NaN       1       NaN  \n",
       "2020-01-21 韩国       0     NaN       1       0.0  \n",
       "2020-01-22 韩国       0     NaN       1       0.0  \n",
       "2020-01-23 韩国       0     NaN       1       0.0  \n",
       "2020-01-24 韩国       0     NaN       2       0.0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# groupby创建层次化索引\n",
    "data = alltime_world.groupby(['日期','名称']).mean()\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b78c20e9",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th>名称</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-05</th>\n",
       "      <th>中国</th>\n",
       "      <td>24433</td>\n",
       "      <td>23260</td>\n",
       "      <td>962</td>\n",
       "      <td>493</td>\n",
       "      <td>0</td>\n",
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       "      <td>962</td>\n",
       "      <td>493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22978</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-06</th>\n",
       "      <th>中国</th>\n",
       "      <td>31161</td>\n",
       "      <td>26359</td>\n",
       "      <td>1540</td>\n",
       "      <td>637</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6728</td>\n",
       "      <td>3230.0</td>\n",
       "      <td>578</td>\n",
       "      <td>144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28984</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <th>中国</th>\n",
       "      <td>34598</td>\n",
       "      <td>27657</td>\n",
       "      <td>2052</td>\n",
       "      <td>723</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3437</td>\n",
       "      <td>0.0</td>\n",
       "      <td>512</td>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31823</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-08</th>\n",
       "      <th>中国</th>\n",
       "      <td>37162</td>\n",
       "      <td>28942</td>\n",
       "      <td>2651</td>\n",
       "      <td>812</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2564</td>\n",
       "      <td>3916.0</td>\n",
       "      <td>599</td>\n",
       "      <td>89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33699</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-09</th>\n",
       "      <th>中国</th>\n",
       "      <td>40224</td>\n",
       "      <td>23589</td>\n",
       "      <td>3282</td>\n",
       "      <td>909</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3062</td>\n",
       "      <td>4008.0</td>\n",
       "      <td>631</td>\n",
       "      <td>97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36033</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "                累计确诊   累计疑似  累计治愈  累计死亡  累计重症  累计境外输入  当日新增确诊  当日新增疑似  当日新增治愈  \\\n",
       "日期         名称                                                                   \n",
       "2020-02-05 中国  24433  23260   962   493     0       0   24433  3971.0     962   \n",
       "2020-02-06 中国  31161  26359  1540   637     0       0    6728  3230.0     578   \n",
       "2020-02-07 中国  34598  27657  2052   723     0       0    3437     0.0     512   \n",
       "2020-02-08 中国  37162  28942  2651   812     0       0    2564  3916.0     599   \n",
       "2020-02-09 中国  40224  23589  3282   909     0       0    3062  4008.0     631   \n",
       "\n",
       "               当日新增死亡  当日新增重症  当日现存确诊  当日新增境外输入  \n",
       "日期         名称                                    \n",
       "2020-02-05 中国     493     NaN   22978       NaN  \n",
       "2020-02-06 中国     144     NaN   28984       0.0  \n",
       "2020-02-07 中国      86     NaN   31823       0.0  \n",
       "2020-02-08 中国      89     NaN   33699       0.0  \n",
       "2020-02-09 中国      97     NaN   36033       0.0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取部分数据\n",
    "data_part = data.loc(axis=0)[:,['中国','韩国','美国','意大利','英国','西班牙','德国']]\n",
    "\n",
    "data_part.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6de80826",
   "metadata": {},
   "outputs": [
    {
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       "      <td>中国</td>\n",
       "      <td>31161</td>\n",
       "      <td>26359</td>\n",
       "      <td>1540</td>\n",
       "      <td>637</td>\n",
       "      <td>0</td>\n",
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       "      <td>6728</td>\n",
       "      <td>3230.0</td>\n",
       "      <td>578</td>\n",
       "      <td>144</td>\n",
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       "      <td>28984</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2020-02-07</th>\n",
       "      <td>中国</td>\n",
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       "      <td>2052</td>\n",
       "      <td>723</td>\n",
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       "      <td>3437</td>\n",
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       "      <td>512</td>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31823</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-08</th>\n",
       "      <td>中国</td>\n",
       "      <td>37162</td>\n",
       "      <td>28942</td>\n",
       "      <td>2651</td>\n",
       "      <td>812</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2564</td>\n",
       "      <td>3916.0</td>\n",
       "      <td>599</td>\n",
       "      <td>89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33699</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-09</th>\n",
       "      <td>中国</td>\n",
       "      <td>40224</td>\n",
       "      <td>23589</td>\n",
       "      <td>3282</td>\n",
       "      <td>909</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3062</td>\n",
       "      <td>4008.0</td>\n",
       "      <td>631</td>\n",
       "      <td>97</td>\n",
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      "text/plain": [
       "            名称   累计确诊   累计疑似  累计治愈  累计死亡  累计重症  累计境外输入  当日新增确诊  当日新增疑似  \\\n",
       "日期                                                                       \n",
       "2020-02-05  中国  24433  23260   962   493     0       0   24433  3971.0   \n",
       "2020-02-06  中国  31161  26359  1540   637     0       0    6728  3230.0   \n",
       "2020-02-07  中国  34598  27657  2052   723     0       0    3437     0.0   \n",
       "2020-02-08  中国  37162  28942  2651   812     0       0    2564  3916.0   \n",
       "2020-02-09  中国  40224  23589  3282   909     0       0    3062  4008.0   \n",
       "\n",
       "            当日新增治愈  当日新增死亡  当日新增重症  当日现存确诊  当日新增境外输入  \n",
       "日期                                                    \n",
       "2020-02-05     962     493     NaN   22978       NaN  \n",
       "2020-02-06     578     144     NaN   28984       0.0  \n",
       "2020-02-07     512      86     NaN   31823       0.0  \n",
       "2020-02-08     599      89     NaN   33699       0.0  \n",
       "2020-02-09     631      97     NaN   36033       0.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将层级索引还原\n",
    "data_part.reset_index('名称',inplace=True)\n",
    "\n",
    "data_part.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8fa306a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制多个国家的累计确诊人数折线图\n",
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "data_part.groupby('名称')['累计确诊'].plot(legend=True,marker='o',ms=3,lw=1)\n",
    "\n",
    "ax.xaxis.set_major_locator(dates.MonthLocator())    #设置间距\n",
    "ax.xaxis.set_major_formatter(dates.DateFormatter('%b'))    #设置日期格式\n",
    "\n",
    "fig.autofmt_xdate()    #自动调整日期倾斜\n",
    "\n",
    "plt.title('各国新冠肺炎累计确诊病例折线图',size=15)\n",
    "plt.ylabel('人数')\n",
    "plt.grid(axis='y')\n",
    "plt.box(False)\n",
    "plt.legend(bbox_to_anchor = [1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8f3986e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 解决坐标轴负号乱码问题\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "# 绘制各国新增确诊人数折线图\n",
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "data_part[data_part.index>'2021-04'].groupby('名称')['当日新增确诊'].plot(legend=True,marker='o',ms=3,lw=1)\n",
    "\n",
    "ax.xaxis.set_major_locator(dates.MonthLocator())    #设置间距\n",
    "ax.xaxis.set_major_formatter(dates.DateFormatter('%b'))    #设置日期格式\n",
    "\n",
    "fig.autofmt_xdate()    #自动调整日期倾斜\n",
    "\n",
    "plt.title('各国新冠肺炎新增确诊病例折线图',size=15)\n",
    "plt.ylabel('人数')\n",
    "plt.grid(axis='y')\n",
    "plt.box(False)\n",
    "plt.legend(bbox_to_anchor = [1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "950f5b27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Korea = alltime_world[alltime_world['名称']=='韩国']\n",
    "# 取从2021年04月开始的数据\n",
    "Korea = Korea[Korea.index > '2021-04']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "Korea['累计确诊'].plot(ax=ax, fontsize=10, style='-',lw=1,color='c',marker='o',ms=3,legend=True)\n",
    "ax.set_ylabel('人数', fontsize=10)\n",
    "\n",
    "ax1 = ax.twinx()\n",
    "ax1.bar(Korea.index, Korea['当日新增确诊'])\n",
    "ax1.xaxis.set_major_locator(dates.DayLocator(interval = 5))\n",
    "ax1.xaxis.set_major_formatter(dates.DateFormatter('%b %d'))\n",
    "ax1.legend(['当日新增确诊'],loc='upper left',bbox_to_anchor=(0.001, 0.9))\n",
    "plt.grid(axis='y')\n",
    "plt.box(False)\n",
    "plt.title('韩国新冠肺炎疫情折线图',size=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83288207",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
